MLflow framework

最近做了MLflow [https://mlflow.org/docs/latest/index.html]和公司内部有监督建模流程的集成工作。记录一些关键信息。

MLflow simple use

import mlflow

with mlflow.start_run() as run:
    # Some Training

Get or Create Experiment

mlflow.set_experiment(EXPERIMENT_NAME)
experiment_id = mlflow.get_experiment_by_name(EXPERIMENT_NAME).experiment_id

Download artifact

import json
client = MlflowClient()
previous_run_id = "dbc4eab36cba4b97b1b194fa555c7d94"
_ = client.download_artifacts(previous_run_id, "data/dataset_loc_map.json",".")
dataset_loc_map = json.load(open("data/dataset_loc_map.json"))

Log after run

client = MlflowClient()
client.log_metric(run.info.run_id, "test_metric",0.5)
client.log_param(run.info.run_id, "test_param",0.5)

Start tracking server

127.0.0.1只有localhost访问。0.0.0.0可以用本机ip访问，然后可以做nodeport with k8s。 ```shell script mlflow server –backend-store-uri sqlite:////Users/jiayuzhai/Documents/Trial/sml_project/mlflow2.db –default-artifact-root s3://some-bucket/mlflow/mlruns –host 127.0.0.1

## Log model
```python
mlflow.xgboost.log_model(bst,artifact_path="model",registered_model_name="SML model A")

Written on July 29, 2021